Mis on vasturünnaktreening kaitseks?

Adversarial training is a method to increase the robustness of machine learning models, especially neural networks, against so-called adversarial attacks. These attacks create specially modified input data (adversarial examples) that look almost like normal data but deceive the model, causing it to make errors.

The essence of adversarial training is that during training, these distorted examples are added to the normal data, and the model learns to classify them correctly. This helps the model become more resistant to such attacks in the future.

Example:

# Pseudocode for adversarial training
for batch in data_loader:
    x, y = batch
    x_adv = generate_adversarial_examples(x, y, model)
    loss = loss_fn(model(x), y) + loss_fn(model(x_adv), y)
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

Thus, adversarial training is a way to protect the model, increasing its reliability and security when working with potentially harmful input data.